期刊文献+
共找到1篇文章
< 1 >
每页显示 20 50 100
Enabling Virtual Met Masts for wind energy applications through machinelearning-methods
1
作者 sandra schwegmann Janosch Faulhaber +4 位作者 Sebastian Pfaffel Zhongjie Yu Martin Dörenkämper Kristian Kersting Julia Gottschall 《Energy and AI》 2023年第1期26-42,共17页
As wind is the basis of all wind energy projects, a precise knowledge about its availability is needed. For ananalysis of the site-specific wind conditions, Virtual Meteorological Masts (VMMs) are frequently used. VMM... As wind is the basis of all wind energy projects, a precise knowledge about its availability is needed. For ananalysis of the site-specific wind conditions, Virtual Meteorological Masts (VMMs) are frequently used. VMMsmake use of site calibrated numerical data to provide precise wind estimates during all phases of a wind energyproject. Typically, numerical data are used for the long-term correlation that is required for estimating theyield of new wind farm projects. However, VMMs can also be used to fill data gaps or during the operationalphase as an additional reference data set to detect degrading sensors. The value of a VMM directly dependson its ability and precision to reproduce site-specific environmental conditions. Commonly, linear regressionis used as state of the art to correct reference data to the site-specific conditions. In this study, a frameworkof 10 different machine-learning methods is tested to investigated the benefit of more advanced methods ontwo offshore and one onshore site. We find significantly improving correlations between the VMMs and the reference data when using more advanced methods and present the most promising ones. The K-NearestNeighbors and AdaBoost regressors show the best results in our study, but Multi-Output Mixture of GaussianProcesses is also very promising. The use of more advanced regression models lead to decreased uncertainties;hence those methods should find its way into industrial applications. The recommended regression models canserve as a starting point for the development of end-user applications and services. 展开更多
关键词 Virtual Met Mast(VMM) Wind power Machine learning Reanalysis data Site assessment Weather Research and Forecasting Model(WRF) Measure-Correlate-Predict(MCP)
原文传递
上一页 1 下一页 到第
使用帮助 返回顶部